Linking Named Entities in Dutch Historical Newspapers

We improved access to the collection of Dutch historical newspapers of the Koninklijke Bibliotheek by linking named entities in the newspaper articles to corresponding Wikidata descriptions by means of machine learning techniques and crowdsourcing. Indexing the Wikidata identifiers for named entities together with the newspaper articles opens up new possibilities for retrieving articles that mention these resources and searching the newspaper collection using semantic relations from Wikidata. In this paper we describe our steps so far in setting up this combination of entity linking, machine l... Mehr ...

Verfasser: van Veen, Theo
Lonij, Juliette
Faber, Willem Jan
Dokumenttyp: conferencePaper
Erscheinungsdatum: 2016
Verlag/Hrsg.: Zenodo
Schlagwörter: enrichment / named entities / linked data / entity linking / semantic search / machine learning / crowdsourcing
Sprache: unknown
Permalink: https://search.fid-benelux.de/Record/base-29466328
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://doi.org/10.5281/zenodo.843504

We improved access to the collection of Dutch historical newspapers of the Koninklijke Bibliotheek by linking named entities in the newspaper articles to corresponding Wikidata descriptions by means of machine learning techniques and crowdsourcing. Indexing the Wikidata identifiers for named entities together with the newspaper articles opens up new possibilities for retrieving articles that mention these resources and searching the newspaper collection using semantic relations from Wikidata. In this paper we describe our steps so far in setting up this combination of entity linking, machine learning and crowdsourcing in our research environment as well as our planned activities aimed at improving the quality of the links and extending the semantic search capabilities. ; The final publication is available at Springer via https://doi.org/10.1007/978-3-319-49157-8_18.